Support Conditional Hyperparameter in AMT

**Describe the feature you'd like**

Hyperparameter tuning frameworks like [optuna](https://optuna.org/) and google's [vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview#conditional_hyperparameters) all support some form of conditional hyperparameter. Could you consider supporting this feature in some capacity?

**How would this feature be used? Please describe.**

For instance, in training a cnn, we can define hyperparameters conditional on their parent hyperparameter matching some condition:

```
search_space = {}
if optimizer == 'adam':
search_space['adam_beta_1'] = range(...)
search_space['adam_beta_2'] = range(...)
elif optimizer == 'sgd':
...
```

**Describe alternatives you've considered**

Right now there are a few ways to work around this:

* Using optuna with sagemaker
* Enumerate all possible hyperparameters and handle appropriately in the training entry point (i.e., ignore the ones not needed)

It would be very helpful if this feature is supported by default. Thanks.

Aucun fichier, test ou point d’entrée n’est indiqué. Commencez par examiner le flux existant de réglage des hyperparamètres d’AMT ainsi que la documentation liée d’Optuna et de Vertex AI sur les hyperparamètres conditionnels, puis définissez comment l’espace de recherche conditionnel de l’exemple CNN doit être représenté et validé.

Rédigé par le modèle d'indexation à partir du texte de l'issue.